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list_ai_connections

Read-only

Every AI connection to this workspace (administrators): MCP keys — whose they are, who made them (the owner, or an admin who issued it to them), scopes, limits, last used, and whether each is awaiting its owner's claim, active, expired or revoked — and connected apps that signed in with OAuth (Claude, ChatGPT…).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_idNoOnly this person's keys
include_inactiveNoAlso expired, revoked and never-claimed keys

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. Beyond that, the description usefully discloses the substance returned — key ownership, issuing admin vs owner, scopes, limits, last-used, and the claim/active/expired/revoked lifecycle — which is real behavioral context an agent would not get from the annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence, front-loaded with the resource and scope before the enumeration of returned fields. It is dense and dash-heavy, but each clause adds a distinct piece of information rather than padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only listing tool with full schema coverage and an explicit read-only annotation, the description supplies the essential missing piece — what the returned connections consist of and their lifecycle states. With no output schema, it could say slightly more about volume/pagination, but it is otherwise complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both parameters are already documented in the schema. The description doesn't add syntax or format detail for user_id or include_inactive, though its mention of the claim/expired/revoked states loosely maps to include_inactive. Baseline 3 applies when the schema carries the semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific resource ("Every AI connection to this workspace") and enumerates exactly what it returns: MCP keys with owner/issuer, scopes, limits, last-used, status, plus OAuth-signed-in apps like Claude and ChatGPT. It clearly reads as a connection-inventory tool, distinct from activity or usage tools, though it does not explicitly name a sibling it should not be confused with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The parenthetical "(administrators)" signals the intended audience, implying an admin-only audit/inventory use case. However, there is no explicit when-to-use versus a sibling like ai_activity_log or ai_usage_summary, nor any stated prerequisites, so the guidance is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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